>Frequent LLM users already know not to do that.
And I think that’s the biggest problem. Amidst the current push to utilize LLMs across orgs and groups there are a large (if even say majority) of people that are using them every day but who have never approached anything as technical as a “harness” before let alone an entire setup.
For them the behavior mentioned here is a major issue.
So the reasonable response to being told you're holding your scissors wrong is to realize that yes, you most likely are holding your scissors wrong[0], and ask the other person for advice (or just to do the thing), or look up a YouTube video and learn, or sign up to a class, or such.
Expecting mastery in 30 seconds is not a reasonable attitude, but it's unfortunately the lie that software industry tried to sell to people for the past 15 years or so.
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[0] - There's much more to it than one would think.
I don’t have an example off hand, but I know that it’s easy to dismiss something an LLM does as trivial if your work is extremely marginal. Most devs aren’t creating their own programming languages. I can’t help but think people who hold this opinion also think the work most software professionals do is “trivial” (“you’re just moving strings around, that’s not impressive/trivial”)
A lathe operator isn’t any good if they don’t frequently operate lathes.
A articulated robot implementer needs frequent experience implementing robots to be any good.
That doesn’t mean lathes or robots are useless. Nor does it mean they have failed as products because they require expertise.
I do think it raises questions as to whether vast swathes of the population will be effective at using LLMs. Are they scissors, or a lathe?
Perhaps that craft of using the exact subset of English has something to do with the correct selection of words and concise, yet expressive enough, expressions, in a fashion resembling creating a code.
A code that's meant to be understood by machines, we could call it "computer code". And said computer code could be used to create recipes, algorithms, let's call them "programs". Hey, I think I have ideas for 2 possible names for this process!
But seriously, you can put a shebang on an english text file now (if you're sufficiently brave), or feed it through something that spits out code on the other end (so you can proof read the consequences before executing them).
It's crazy, but this is 2026, and that actually ... just works. You can even do it locally, if you don't mind running a space heater.
Thing is, when you have the expressiveness and power of a full natural language (and you're already paying for it), why would you want to constrain yourself to a subset? That's not very practical. Why not use all of it? Computing was never about typing code into machines anyway. "Computer" used to be a human profession, until it got automated.
On the upside, there's thousands of years of documentation. On the downside, a lot said documentation is underspecified and/or straight wishful thinking. It's certainly an interesting avenue to explore.
For the same reason math, physics, chemistry, etc figured out a long time ago that Koine Greek, Latin, French, German, English, etc aren't the best languages for science. Constraint gives focus, precision.
If you code novels, knock yourself out.
None-exhaustively:
Python has if, for, while, def, class and first class lists, dicts , functions ;
Forth has this stack machine concept, RPN, compilation-in-the-REPL when defining new functions.
Lisp has this code is data is code concept, and CAR, CDR, first class lists (obviously ), first class functions (in some of them) ... etc.
Machine code can (theoretically) be directly expressed in logic gates.
How about a quick look at what English supports:
Conditionals, iteration, abstraction, composition, delegation, exception handling, scope, naming, modularity; intent, priority, graduated precision, analogy, context-dependence; And.. the concept of semantic triples is built in as a syntactic primitive (subject-predicate-object), so you can even do a bunch of GOFAI right off the bat.
It's weird thinking of english as a programming language. But it kind of works like one if you want to, and computers can process it now?
I'm talking about the opposite problem: these supersets are ambiguous, contradictory, vague. At the end of the day the thing that is programmed needs to be clear, unambiguous and ideally concise, too (performance in its million incarnations).
So yeah, I guess you can fix the ambiguous aspect with verbosity. Just write more words until you define everything you need to define more directly when using a formal language.
I would be extremely shocked if programming wouldn't require knowing a very specific, albeit huge, domain jargon.
English assumes the target is an agent with memory/state in a given context. Ambiguity, verbosity, noise is strongly reduced by means of modelling the other agent's state, then only transmitting the required state diff. The receiver decodes by comparing the diff against the other side's predicted state and updating. [1] This kind of protocol would obviously be NUTS to build from scratch if you went about it as an engineer I'd think. But we have the hardware and software preinstalled in humans , and now my 3090 can run an (imperfect, but viable) decoder
Is it useful? Yeah, I think it actually is. English is able to encode things that are ambiguous, contradictory, vague... and get useful results. Not always; maybe not even often. As you say, skill required, but the option is there. Formal languages just crash.
It's interesting is what I'd call it.
[1] see also: Clark & Brennan's grounding theory in linguistics; Predictive coding in neuroscience; Delta encoding in compression; and Theory of mind in cognitive science. They all dance around the same shape, so this is roughly accurate I think.
So - you're not wrong that eg. mathematical notation is (often) used, as we both very well know. But English is really quite prominent!
And now computers can process both, where before they couldn't.
The engineering doesn't go away, not yet. Decomposition, abstraction, state management, blast radius containment O:-) . But now you can express much more of that in the language the arxiv papers are already written in.
That inspired me to figure out how to do exactly that:
https://til.simonwillison.net/llms/llm-shebang
#!/usr/bin/env -S llm -f
Generate an SVG of a pelican riding a bicycle
The funky thing is that it's not just English. I could vibe code in Romanian, it would probably be hilarious :-)) Probably not for whomever would have to take over the app, though.
If it wasn't on your bucket list to begin with, who cares: Now you can add it and complete it in one fell swoop ;-)
To me learning to use LLMs is the same as doing anything else, you have to practice and put in the hours to get good. Maybe some harnesses will eventually allow LLMs to function more as scissors than lathes. This seems to be what Microsoft is trying to do by embedding Copilot in all their products and saying “choose the UI that works best for you”. If that doesn’t end up working we’ll need another paradigm for “non-technical” users to effectively operate computer assistants